Questions tagged [doc2vec]
The doc2vec tag has no usage guidance.
23
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Word Embeddings / Doc2Vec on unordered bag of keywords
I have a corpus of documents containing unordered lists of keywords (as document description).
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1
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868
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Difference between Doc2Vec and BERT
I am trying to understand the difference between Doc2Vec and BERT. I do understand that doc2vec uses a paragraph ID which also serves as a paragraph vector. I am not sure though if that paragraph ID ...
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65
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Treating Word Embeddings as Multivariate Gaussian Random Variables
I want to specify some probabilistic clustering model (such as a mixture model or lda) over words, and instead of using the traditional method of representing words as an indicator vector , I want to ...
- 23
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18
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Is there a way to train Doc2Vec on a corpus of docs and be able to take a novel doc and see how similar it is to the trained corpus?
I have a project idea, where I train a bunch of documents on Doc2Vec and then take a novel, input doc, and ideally be able to be told how similar it is to the docs supplied for training as a whole or ...
- 133
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235
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How to examine if a Doc2Vec model is sufficiently trained?
I started experimenting with gensim's Doc2Vec for sentiment analysis. For the training of the embedding itself, I have seen examples using a reduced learning rate with a few 10s or even a few hundred ...
- 131
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43
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Imbalanced Classification: BOW vs doc2Vec in XGBoost with sample weights
I am new to machine learning. I have an imbalanced dataset of pages of reports with
class 1: 97%,
class 2: 2.2%
class 3: 0.25%
which are the different type of pages
I am mostly concerned with ...
- 159
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2
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157
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classification of similar text input features with text output label
I hope somebody can provide guidance/input/advice on my project, where I believe AI can help.
I have a general understanding of AI, but I lack a formal training.
I've never built a neural net from ...
- 73
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38
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Preprocessing for Document Similarity Using Doc2Vec
I'm trying to determine document similarity using Doc2Vec on a large series of legal opinions, which can contain some highly jargonistic language and phrases (e.g. en banc, de novo, etc.). I'm ...
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477
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Embedding from Transformer-based model from paragraph or documnet (like Doc2Vec)
I have a set of data that contains the different lengths of sequences. On average the sequence length is 600. The dataset is like this:
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84
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What is the meaning of, or explanation for, having multiple tags in a Doc2Vec model's TaggedDocuments?
I've tried reading the other answers on this topic but I'm unsure if I understand completely.
For my dataset, I have a series of tagged documents, "good" or "bad." Each document ...
- 11
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56
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Word2Vec vs. Doc2Vec Word Vectors
I am doing some analysis on document similarity and was also interested in word similarity. I know that doc2vec inherits from word2vec and by default trains using word vectors which we can access.
My ...
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295
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Clustering using both text and numerical features
I have a dataset that contains 2 types of features, one is generated from doc2vec and one is numerical feature. I would like to perform clustering analysis on them. However, due to the size of doc2vec ...
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46
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doc2vec - paragraph or article as document
I'm trying to train a doc2vec model on the German wiki corpus. While looking for the best practice I've found different possibilities on how to create the training data.
Should I split every Wikipedia ...
- 143
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25
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Vector representation of documents for text classification
I'm looking for proper method of document embeddings. I know that doc2vec will give me the vector representations for given corpus, but how do I embed new documents? I need to train neural network ...
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150
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DBSCAN on textual and numerical columns
I have a dataset which has two columns:
title price
sentence1 12
sentence2 13
I have used doc2vec to convert the ...
- 411
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31
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Document Similarity to List of Words in Sentiment Analysis [closed]
How would you go about finding document similarity to a list of words in Sentiment Analysis?
Looking find document similarity to multiple lists of words in sentiment analysis. I had been working on ...
- 111
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2
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418
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Word2Vec with CNN
I am trying to classify documents using CNN (convolutional neural network) with Word2Vec embeddings. However to do this, it requires me to trim all texts to the same length. I just pad all the ...
- 31
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47
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Topic alignment / topic modelling
What is the most efficient method for detecting whether the article is mostly about a specific topic, but without lots of data for training? My task is to determine how much a document is e.g. about ...
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236
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How to implement LSTM using Doc2Vec vectors to get representation?
Hi all. I'm a newbie in ML. I read and found a paper about A Multi-Level Plagiarism Detection System Based on Deep Learning Algorithms and want to implement this model . But I can't find more about ...
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83
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T-SNE good clustering but SVM classification poor
I am trying to classify in 4 different classes, paragraph embedding vector computed with doc2vec using an non-linear svm over them.
When I visualize the embeddings using tensorboard t-sne I can see ...
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1
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2k
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Use embeddings to find similarity between documents
I need to find cosine similarity between two text documents. I need embeddings that reflect order of the word sequence, so I don't plan to use document vectors built with bag of words or TF/IDF. ...
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158
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Approach to semantic similarity between documents
I was wondering what approach people would take, or point me in the right direction on this challenge I have set myself. I am pretty new at this, I have covered some area but want to expand my ...
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Can feature representation acquired by a same model but trained on different corpus be used on the same classification model?
For example, if I wanna do document classification with doc2vec embeddings, first I train the training set to get doc2vec embeddings, and fit the embeddings to a classification model; later on when I ...
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